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Knowledge engineering (KE) refers to all aspects involved in knowledge-based systems.

Background

Expert systems

One of the first examples of an expert system was MYCIN, an application to perform medical diagnosis. In the MYCIN example, the domain experts were medical doctors and the knowledge represented was their expertise in diagnosis.

Expert systems were first developed in artificial intelligence laboratories as an attempt to understand complex human decision making. Based on positive results from these initial prototypes, the technology was adopted by the US business community (and later worldwide) in the 1980s. The Stanford heuristic programming project led by Edward Feigenbaum was one of the leaders in defining and developing the first expert systems.

History

In the earliest days of expert systems, there was little or no formal process for the creation of the software. Researchers just sat down with domain experts and started programming, often developing the required tools (e.g. inference engines) at the same time as the applications themselves. As expert systems moved from academic prototypes to deployed business systems it was realized that a methodology was required to bring predictability and control to the process of building the software. There were essentially two approaches that were attempted:

Use conventional software development methodologies
Develop special methodologies tuned to the requirements of building expert systems

Many of the early expert systems were developed by large consulting and system integration firms such as Andersen Consulting. These firms already had well tested conventional waterfall methodologies (e.g. Method/1 for Andersen) that they trained all their staff in and that were virtually always used to develop software for their clients. One trend in early expert systems development was to simply apply these waterfall methods to expert systems development.

Another issue with using conventional methods to develop expert systems was that due to the unprecedented nature of expert systems, they were one of the first applications to adopt rapid application development methods that feature iteration and prototyping as well as or instead of detailed analysis and design. In the 1980s few conventional software methods supported this type of approach.

The final issue with using conventional methods to develop expert systems was the need for knowledge acquisition. Knowledge acquisition refers to the process of gathering expert knowledge and capturing it in the form of rules and ontologies. Knowledge acquisition has special requirements beyond the conventional specification process used to capture most business requirements.

These issues led to the second approach to knowledge engineering: the development of custom methodologies specifically designed to build expert systems.^[1] One of the first and most popular of such methodologies custom designed for expert systems was the Knowledge Acquisition and Documentation Structuring (KADS) methodology developed in Europe. KADS had great success in Europe and was also used in the United States.^[2]

References

^ Feigenbaum, Edward; McCorduk, Pamela (1983). The Fifth Generation (1st ed.). Reading, MA: Addison-Wesley. ISBN 978-0-201-11519-2. OCLC 9324691.
^ Schreiber, August Th.; Akkermans, Hans; Anjewierden, Anjo; Dehoog, Robert; Shadbolt, Nigel; Vandevelde, Walter; Wielinga, Bob (2000), Knowledge engineering and management: the CommonKADS methodology (1st ed.), Cambridge, MA: The MIT Press, ISBN 978-0-262-19300-9

External links

Data & Knowledge Engineering – Elsevier Journal
Knowledge Engineering Review, Cambridge Journal
The International Journal of Software Engineering and Knowledge Engineering – World Scientific
IEEE Transactions on Knowledge and Data Engineering
Expert Systems: The Journal of Knowledge Engineering – Wiley-Blackwell

[1] Feigenbaum, Edward; McCorduk, Pamela (1983). The Fifth Generation (1st ed.). Reading, MA: Addison-Wesley. ISBN 978-0-201-11519-2. OCLC 9324691.

[2] Schreiber, August Th.; Akkermans, Hans; Anjewierden, Anjo; Dehoog, Robert; Shadbolt, Nigel; Vandevelde, Walter; Wielinga, Bob (2000), Knowledge engineering and management: the CommonKADS methodology (1st ed.), Cambridge, MA: The MIT Press, ISBN 978-0-262-19300-9

[1]

[2]

@@ Line 1: / Line 1: @@
-{{about|methods for developing expert systems|application of knowledge based technology to the domain of manufacturing and CAD|Knowledge based engineering}}
+{{Short description|Methods for developing expert systems}}
+{{about|methods for developing expert systems|application of knowledge based technology to the domain of manufacturing and CAD|Knowledge-based engineering}}
-Knowledge Engineering (KE) refers to all technical, scientific and social aspects involved in building, maintaining and using [[knowledge-based systems]].
+'''Knowledge engineering''' ('''KE''') refers to all aspects involved in [[knowledge-based systems]].
-== Background: Expert Systems ==
-One of the first examples of an expert system was [[MYCIN]], an application to perform medical diagnosis. In the MYCIN example the domain experts were medical doctors and the knowledge represented was their expertise in diagnosis.
+== Background ==
-Expert systems were first developed in artificial intelligence laboratories as an attempt to understand complex human decision making. Based on positive results from these initial prototypes the technology was adopted by the US business community (and later world wide) in the 1980s. The Stanford heuristic programming projects led by [[Edward Feigenbaum]] was one of the leaders in defining and developing the first expert systems.
+=== Expert systems===
+One of the first examples of an [[expert system]] was [[MYCIN]], an application to perform medical diagnosis. In the MYCIN example, the domain experts were medical doctors and the knowledge represented was their expertise in diagnosis.
+Expert systems were first developed in artificial intelligence laboratories as an attempt to understand complex human decision making. Based on positive results from these initial prototypes, the technology was adopted by the US business community (and later worldwide) in the 1980s. The Stanford heuristic programming project led by [[Edward Feigenbaum]] was one of the leaders in defining and developing the first expert systems.
 == History ==
-In the earliest "cowboy" days of expert systems there was little or no formal process for the creation of the software. Researchers just sat down with domain experts and started programming, often developing the required tools (e.g. [[inference engine|inference engines]]) at the same time as the applications themselves. As expert systems moved from academic prototypes to deployed business systems it was realized that a methodology was required to bring predictability and control to the process of building the software. There were essentially two approaches that were attempted:
+In the earliest days of expert systems, there was little or no formal process for the creation of the software. Researchers just sat down with domain experts and started programming, often developing the required tools (e.g. [[inference engine]]s) at the same time as the applications themselves. As expert systems moved from academic prototypes to deployed business systems it was realized that a methodology was required to bring predictability and control to the process of building the software. There were essentially two approaches that were attempted:
 # Use conventional software development methodologies
 # Develop special methodologies tuned to the requirements of building expert systems
 Many of the early expert systems were developed by large consulting and system integration firms such as [[Andersen Consulting]]. These firms already had well tested conventional [[Waterfall model|waterfall methodologies]] (e.g. Method/1 for Andersen) that they trained all their staff in and that were virtually always used to develop software for their clients. One trend in early expert systems development was to simply apply these waterfall methods to expert systems development.
-Another issue with using conventional methods to develop expert systems was that due to the unprecedented nature of expert systems they were one of the first applications to adopt [[rapid application development]] methods that feature iteration and prototyping as well as or instead of detailed analysis and design. In the 1980s few conventional software methods supported this type of approach.
+Another issue with using conventional methods to develop expert systems was that due to the unprecedented nature of expert systems, they were one of the first applications to adopt [[rapid application development]] methods that feature iteration and prototyping as well as or instead of detailed analysis and design. In the 1980s few conventional software methods supported this type of approach.
-The final issue with using conventional methods to develop expert systems was the need for knowledge acquisition. [[Knowledge acquisition]] refers to the process of gathering expert knowledge and capturing it in the form of rules and ontologies. Knowledge acquisition has special requirements beyond the conventional specification process used to capture most business requirements.
+The final issue with using conventional methods to develop expert systems was the need for [[knowledge acquisition]]. ''Knowledge acquisition'' refers to the process of gathering expert knowledge and capturing it in the form of rules and ontologies. Knowledge acquisition has special requirements beyond the conventional specification process used to capture most business requirements.
-These issues led to the second approach to knowledge engineering: development of custom methodologies specifically designed to build expert systems.<ref>{{cite book|last1=Feigenbaum|first1=Edward|last2=McCorduk|first2=Pamela|title=The Fifth Generation| edition = 1st | year = 1983 | publisher = [[Addison-Wesley]] | location = [[Reading, MA]] | isbn = 978-0-201-11519-2 | oclc = 9324691}}</ref> One of the first and most popular of such methodologies custom designed for expert systems was the [[Knowledge Acquisition and Documentation Structuring]] (KADS) methodology developed in Europe. KADS had great success in Europe and was also used in the United States.<ref>{{citation | last = Schreiber | first = August Th. | last2 = Akkermans | first2 = Hans | last3 = Anjewierden | first3 = Anjo | last4 = Dehoog | first4 = Robert | authorlink4 = Robert de Hoog (scientist) | last5 = Shadbolt | first5 = Nigel | authorlink5 =  Nigel Shadbolt | last6 = Vandevelde | first6 = Walter | last7 = Wielinga | first7 = Bob | authorlink7 =  Bob Wielinga | title = Knowledge engineering and management: the CommonKADS methodology | edition = 1st | year = 2000 | publisher = The MIT Press | location = [[Cambridge, MA]] | isbn = 978-0-262-19300-9 }}</ref>
+These issues led to the second approach to knowledge engineering: the development of custom methodologies specifically designed to build expert systems.<ref>{{cite book|last1=Feigenbaum|first1=Edward|last2=McCorduk|first2=Pamela|title=The Fifth Generation|url=https://archive.org/details/fifthgenerationa00feigrich|url-access=registration| edition = 1st | year = 1983 | publisher = [[Addison-Wesley]] | location = [[Reading, MA]] | isbn = 978-0-201-11519-2 | oclc = 9324691}}</ref> One of the first and most popular of such methodologies custom designed for expert systems was the [[Knowledge Acquisition and Documentation Structuring]] (KADS) methodology developed in Europe. KADS had great success in Europe and was also used in the United States.<ref>{{citation | last = Schreiber | first = August Th. | last2 = Akkermans | first2 = Hans | last3 = Anjewierden | first3 = Anjo | last4 = Dehoog | first4 = Robert | authorlink4 = Robert de Hoog (scientist) | last5 = Shadbolt | first5 = Nigel | authorlink5 =  Nigel Shadbolt | last6 = Vandevelde | first6 = Walter | last7 = Wielinga | first7 = Bob | authorlink7 =  Bob Wielinga | title = Knowledge engineering and management: the CommonKADS methodology | edition = 1st | year = 2000 | publisher = The MIT Press | location = [[Cambridge, MA]] | isbn = 978-0-262-19300-9 }}</ref>
-==References==
- {{reflist}}
 ==See also==
-* [[Knowledge representation]]
-* [[Knowledge retrieval]]
-* [[Knowledge management]]
 * [[Knowledge level modeling]]
-* [[Knowledge acquisition]]
+* [[Knowledge management]]
+* [[Knowledge representation and reasoning]]
+* [[Knowledge retrieval]]
 * [[Knowledge tagging]]
-* [[Expert systems]]
+* [[Method engineering]]
+==References==
+{{reflist}}
 ==External links==
 * [http://www.elsevier.com/wps/find/journaldescription.cws_home/505608/description#description Data & Knowledge Engineering] – Elsevier Journal
 * [http://journals.cambridge.org/action/displayJournal?jid=KER Knowledge Engineering Review], Cambridge Journal
-* [http://www.ksi.edu/ijsk.html The International Journal of Software Engineering and Knowledge Engineering] – World Scientific
+* [https://web.archive.org/web/20080501180201/http://www.ksi.edu/ijsk.html The International Journal of Software Engineering and Knowledge Engineering] – World Scientific
 * [http://www.informatik.uni-trier.de/~ley/db/journals/tkde/index.html IEEE Transactions on Knowledge and Data Engineering]
-* [http://www.blackwellpublishing.com/journal.asp?ref=0266-4720&site=1 Expert Systems: The Journal of Knowledge Engineering] – Wiley-Blackwell
+* [https://web.archive.org/web/20080612045810/http://www.blackwellpublishing.com/journal.asp?ref=0266-4720&site=1 Expert Systems: The Journal of Knowledge Engineering] – Wiley-Blackwell
-{{Computable knowledge}}
+{{Automated reasoning}}
+{{Authority control}}
 {{DEFAULTSORT:Knowledge Engineering}}
-[[Category:Knowledge engineering|*]]
+[[Category:Knowledge engineering| ]]
 [[Category:Semantic Web]]
 [[Category:Ontology (information science)]]

v t e Knowledge representation and reasoning
Backward chaining Case-based reasoning Commonsense reasoning Forward chaining Model-based reasoning Inference engines Proof assistants Knowledge engineering
Expert systems	CLIPS Connectionist expert systems Expert systems for mortgages Legal expert systems
Reasoning systems	Theorem provers Constraint solvers Deductive classifiers Logic programs Procedural reasoning systems Rule engines
Ontology languages	Attempto Controlled English CycL F-logic FO(.) Knowledge Interchange Format Web Ontology Language
Theorem provers	CARINE E Nqthm Otter Paradox Prover9 SPASS TPS Z3
Constraint satisfaction	Constraint programming Constraint logic programming Local consistency SMT solvers
Automated planning	Motion planning Multi-agent planning Partial-order planning Preference-based planning Reactive planning State space planning